10 Expert Tips to Optimize LLM Inference Cost and Scale Smarter

As you scale your LLM applications, you may notice a slow but steady increase in your cloud costs, which is often caused by the booming costs of inference. Inference costs can skyrocket as you serve more users or process more data, whether running a few LLM queries on an isolated instance or running thousands of queries across multiple servers to serve an LLM application in production. The good news is that there are ways to mitigate these costs, and this article will outline a few strategies to help you get started. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

One solution for addressing inference costs is to use AI inference APIs. These tools can help you run high-performing LLM applications at scale while keeping inference costs low and predictable.

What are the Factors Influencing LLM Inference Costs?

The processing and energy costs associated with Large Language Models (LLMs) like GPT-4, Claude 3.5, and LLAMA are considerable. While I was aware of these costs, I hadn’t fully explored the extent to which the growing use of these tools could significantly impact:

• Energy consumption
• Pollution
• Overall sustainability

The demand for computational power has been rising steadily. Still, AI models, extensive language and vision models, have unique characteristics that introduce distinct costs compared to traditional computing tasks. Before diving into the details, it’s helpful to distinguish between two key categories of AI-related costs.

Cost Type 1 — Training Costs

First, let's discuss the costs associated with training a language model. This process involves unsupervised learning, where neural networks and specialized algorithms analyze vast amounts of text data and “learn” from it. The outcome is a set of parameters, weights, and activations that guide the model in generating text.

These models have grown significantly in size recently. The models we’re focused on today are extensive. Training and hosting these models require thousands of advanced computers and extensive storage. These costs are incurred long before the model receives a request from an external user and begins to fulfill its purpose.

A Familiar Example: ChatGPT and Its Associated Costs

Training a large language model (LLM) is an incredibly resource-intensive process. Take OpenAI's ChatGPT, for example. Training GPT-3, which has 175 billion parameters, required 355 years of single-processor computing time and consumed 284,000 kWh of energy—equivalent to the energy consumption of an average U.S. household over the next 30 years! GPT-4, which powers the latest version of ChatGPT most of us use, is orders of magnitude larger and needs over 50 times more energy to train.

Although the exact numbers remain undisclosed, leaked data suggests GPT-4 consists of eight "mixture of experts" models, each with over 220 billion parameters, totaling more than 1.76 trillion. OpenAI is currently training GPT-5, but only they know how long the process will take and how much hardware and energy will be required to complete the training.

Environmental Impact

Training large models generates significant carbon emissions. For instance, GPT-3 emitted an estimated 552 tCO2eq, equivalent to the emissions produced by 120 passenger cars over a year.

To put that into perspective, your vehicle would make the same carbon emissions over 120 years. If GPT-4 follows similar trends, and with rumors of even larger model sizes for the next generation, the environmental costs of training these models become a growing concern.

Balancing Innovation and Sustainability: The Long-Term Costs of Scaling AI Models

The training costs are not much for something millions of people will use for a long time. And for the potential practical and positive benefits humanity gains from this technology, it’s still worth the energy used.

But how big can the models get before we question that math? Of course, we also have another cost category to consider, which has longer-term implications.

Cost Type 2 — Inference Costs

Usage/query costs are referred to as inference compute costs. Inference, or using the trained model to generate outputs, also incurs substantial costs. Unlike training, these costs are ongoing and scale with increased usage. Inference cost consists of three main components:

1. Prompt Cost: The prompt cost is proportional to the length of the prompt. Longer prompts incur higher costs. Optimizing the prompt length is essential to managing expenses effectively.
2. Generation Cost: Similar to the prompt cost, the generation cost is proportional to the length of the generated text. The more tokens generated, the higher the cost. This aspect is crucial for applications that require extensive output, as it directly impacts the overall expenditure.
3. Fixed Cost per Query: A fixed cost may be associated with each query, regardless of the prompt or generation length. This fixed cost can add up, especially in high-volume applications.

Tokens per Second

A critical metric is the rate at which a model can process or generate tokens.

It affects:

• Real-Time Responsiveness: Applications requiring immediate responses benefit from higher tokens per second.
• Latency: Lower tokens per second typically result in higher latency, hindering user experience.
• Scalability: Models with higher processing rates can handle more concurrent requests, making them suitable for growing user bases.

Accuracy and Model Size

Choosing the right model size is essential for balancing accuracy and cost. More extensive models may provide higher accuracy but come with increased costs. Smaller models can be more efficient for real-time processing, especially in resource-constrained environments.

Customization and Fine-Tuning

Customization is vital for applications needing to understand specific contexts. Training large models can be complex and may lead to unexpected behaviors. Smaller models trained on targeted data can reduce costs and improve performance.

Cost Metrics

Understanding cost metrics is crucial for managing expenses effectively.

Key metrics include:

• Total Length of Prompts and Generated Texts: Measured in tokens, this metric is particularly relevant for proprietary LLMs accessed via commercial APIs.
• End-to-End Latency: The parallelism of LLM calls can affect the time complexity of a single run.
• Peak Memory Usage and Energy Consumption: These metrics provide insights into the resource demands of LLM operations.

Comparison

The training phase incurs a one-time, very high cost of computing and energy. This phase is characterized by the need for massive computational resources over a relatively short period.

Inference Phase

The inference phase incurs ongoing costs that scale with the number of users and the frequency of model usage. While each inference may consume less energy than the training phase, the cumulative cost over time can be substantial, especially for widely used models and services like ChatGPT.

Balancing the Cost of AI Innovation: Training vs. Inference in LLMs

The training phase of large language models like GPT-4 or LLAMA is undeniably costly in compute and energy. The long-term usage phase (inference) can also be expensive, especially as usage grows.

While training is a one-time, high-cost event, inference costs are ongoing and can accumulate, potentially surpassing the initial training costs if the model is heavily used. Given the substantial investment required for training these models, they must be leveraged to their full potential.

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10 Proven LLM Inference Cost Optimization Best Practices

1. Quantization: Boosting Efficiency While Reducing Resource Usage

Quantization reduces the precision of model weights and activations, decreasing the memory footprint and computational load and resulting in a more compact neural network representation. Instead of using 32-bit floating-point numbers, quantized models can leverage 16-bit or even 8-bit integers.

This technique helps deploy models on edge devices or environments with limited computational power. While quantization may introduce a slight degradation in model accuracy, its impact is often minimal compared to the substantial cost savings.

2. Pruning: Trimming the Fat for Lean, Efficient Models

Pruning involves removing less significant weights from the model, effectively reducing the size of the neural network without sacrificing much in terms of performance. By trimming neurons or connections that contribute minimally to the model’s outputs, pruning helps decrease inference time and memory usage.

Pruning can be performed iteratively during training, and its effectiveness largely depends on the sparsity of the resulting network. This approach is especially beneficial for large-scale models that contain redundant or unused parameters.

3. Knowledge Distillation: Training Smaller Models to Imitate Larger Models

Knowledge distillation is a process where a smaller model, known as the “student,” is trained to replicate the behavior of a more prominent “teacher” model. The student model learns to mimic the teacher’s outputs, allowing it to perform at a level comparable to the teacher despite having fewer parameters.

This technique enables the deployment of lightweight models in production environments, drastically reducing the inference costs without sacrificing too much accuracy. Knowledge distillation is particularly effective for applications that require real-time processing.

4. Batching: Grouping Requests for Faster Processing

Batching is the simultaneous processing of multiple requests, which can lead to more efficient resource utilization and reduced overall costs. By grouping several requests and executing them in parallel, the model’s computation can be optimized, minimizing latency and maximizing throughput. Batching is widely used in scenarios where multiple users or systems need access to the LLM simultaneously, such as customer support chatbots or cloud-based APIs.

5. Model Compression: Reducing Size Without Losing Performance

Model compression techniques like tensor decomposition, factorization, and weight sharing can significantly reduce a model’s size without affecting its performance.

These methods transform the model’s internal representation into a more compact format, decreasing computational requirements and speeding up inference. Model compression is helpful for scenarios where storage constraints or deployment on devices with limited memory are a concern.

6. Early Exiting: Allowing LLMs to Cut Processes Short

Early exiting is a technique that allows a model to terminate computation once it is confident in its prediction. Instead of passing through every layer, the model exits early if an intermediate layer produces a sufficiently confident result.

This approach is efficient in hierarchical models, where each subsequent layer refines the result produced by the previous one. Early exiting can significantly reduce the average number of computations required, reducing inference time and cost.

7. Optimized Hardware: Supercharging Inference with Specialized Machines

Using specialized hardware for AI workloads like GPUs, TPUs, or custom ASICs can greatly enhance model inference efficiency. These devices are optimized for:

• Parallel processing
• Large matrix multiplications
• Everyday operations in LLMs

Leveraging optimized hardware accelerates inference and reduces the energy costs associated with running these models. Choosing the correct hardware configurations for cloud-based deployments can save substantial costs.

8. Caching: Saving Time and Resources By Reusing Previous Results

Caching involves storing and reusing previously computed results, which can save time and computational resources. If a model repeatedly encounters similar or identical input queries, caching allows it to return the results instantly without re-computing them. Caching is especially effective for tasks like auto-complete or predictive text, where many input sequences are similar.

9. Prompt Engineering: Designing Instructions for Efficient Processing

Designing clear and specific instructions for the LLM, known as prompt engineering, can lead to more efficient processing and faster inference times. Well-designed prompts reduce ambiguity, minimize token usage, and streamline the model’s processing.

Prompt engineering is a low-cost, high-impact approach to optimizing LLM performance without altering the underlying model architecture.

10. Distributed Inference: Spreading Workloads Across Multiple Machines

Distributed inference involves spreading the workload across multiple machines to balance resource usage and reduce bottlenecks. This approach is helpful for large-scale deployments, where a single machine can only handle part of the model.

The model can achieve faster response times and handle more simultaneous requests by distributing the computations, making it ideal for cloud-based inference.

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